Gen AI Engineer

Elevance Health•Atlanta, GA

18h•Hybrid

About The Position

The Gen AI Engineer is responsible for analyzing and modeling organizational data for the Artificial Intelligence (AI) function to draw business insights, which can be used to make business decisions. This role requires associates to be in-office 1 - 2 days per week, fostering collaboration and connectivity, while providing flexibility to support productivity and work-life balance. This approach combines structured office engagement with the autonomy of virtual work, promoting a dynamic and adaptable workplace. Elevance Health is a health company dedicated to improving lives and communities – and making healthcare simpler. It is a Fortune 25 company with a longstanding history in the healthcare industry, looking for leaders at all levels of the organization who are passionate about making an impact on members and the communities served. Elevance Health is creating a culture designed to advance its strategy and lead to personal and professional growth for its associates, with values and behaviors that are the root of its culture, driving business outcomes and shared success.

Requirements

Requires a Bachelor’s degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent degree.
4 or more years of experience in configuration management, which would provide an equivalent background.
Advanced Python proficiency.
4+ years of professional hands-on experience leveraging large sets of structured and unstructured data to develop data-driven tactical and strategic analytics and insights using ML, NLP, and computer vision solutions.
4+ years hands-on experience with Python, SQL, Hugging Face, TensorFlow, Keras, PyTorch, and Spark.
Experience with GCP/AWS cloud platforms.
Strong knowledge of and measurable hands-on experience with developing or tuning Large Language Models (LLM) and Generative AI (GAI).
Experience with NLP, LLMs (extractive and generative), fine-tuning and LLM model development.
Experience developing and optimizing high-quality prompts for NLP applications.
Excellent written & verbal communication and stakeholder management skills.
4+ years project leadership experience including Agile project management, Scaled Agile Frameworks (SAFE).
Experience with LLM serving platforms (vLLM, Text Generation Inference, FastAPI).
Experience with Model quantization for LLMs (GPTQ, AWQ, bitsandbytes).
Experience with GPU memory optimization techniques (tensor parallelism, pipeline parallelism).
Experience with LLM caching strategies for inference optimization.
Experience with RAG architecture design and implementation.
Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge.
Experience with Containerization strategies for ML workloads.
Experience with Canary deployments for ML models.

Responsibilities

Applies data extraction, transformation and loading techniques in order to connect large data sets from a variety of sources.
LLM development and fine-tuning strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency.
Develop roadmap and strategy for NLP, LLM, Gen AI model development and lifecycle implementation.
Responsible for the design and development of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM model development and ensure the end-to-end solution meets all technical and business requirements, and SLA specifications.
Work closely with the MLOps team to create and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, and UAT.
Identify and implement model optimizations to improve system efficiency.
Collaborate closely with the MLOps, product teams, business stakeholders, machine learning engineers, and software engineers for the deployment of machine learning models into production environments, ensuring smooth integration, reliability and scalability.
Ensure the use of standards, governance and best practices in ML model development, and adherence to model and data governance standards.